The effect of sample size in studies of soil microbial community structure

Ranking environmental and edaphic attributes driving soil microbial community structure and activity with special attention to spatial and temporal scales

The incredibly complex soil microbial communities at small scales make their analysis and identification of reasons for the observed structures challenging. Microbial community structure is mainly a result of the inoculum (dispersal), the selective advantages of those organisms under the habitat-based environmental attributes, and the ability of those colonizers to sustain themselves over time. Since soil is protective, and its microbial inhabitants have long adapted to varied soil conditions, significant portions of the soil microbial community structure are likely stable. Hence, a substantial portion of the community will not correlate to often measured soil attributes. We suggest that the drivers be ranked on the basis of their importance to the fundamental needs of the microbes: (i) those that supply energy, i.e., organic carbon and electron acceptors; (ii) environmental effectors or stressors, i.e., pH, salt, drought, and toxic chemicals; (iii) macro-organism associations, i.e., plants and their seasonality, animals and their fecal matter, and soil fauna; and (iv) nutrients, in order, N, P, and probably of lesser importance, other micronutrients, and metals. The relevance of drivers also varies with spatial and time scales, for example, aggregate to field to regional, and persistent to dynamic populations to transcripts, and with the extent of phylogenetic difference, hence phenotypic differences in organismal groups. We present a summary matrix to provide guidance on which drivers are important for particular studies, with special emphasis on a wide range of spatial and temporal scales, and illustrate this with genomic and population (rRNA gene) data from selected studies.

Microbial-Based Products to Control Soil-Borne Pathogens: Methods to Improve Efficacy and to Assess Impacts on Microbiome

Microbial-based products (either as biopesticide or biofertilizers) have a long history of application, though their use is still limited, mainly due to a perceived low and inconsistent efficacy under field conditions. However, their efficacy has always been compared to chemical products, which have a completely different mechanism of action and production process, following the chemical paradigm of agricultural production. This paradigm has also been applied to regulatory processes, particularly for biopesticides, making the marketing of microbial-based formulations difficult. Increased knowledge about bioinocula behavior after application to the soil and their impact on soil microbiome should foster better exploitation of microbial-based products in a complex environment such as the soil. Moreover, the multifunctional capacity of microbial strains with regard to plant growth promotion and protection should also be considered in this respect. Therefore, the methods utilized for these studies are key to improving the knowledge and understanding of microbial-based product activity and improving their efficacy, which, from farmers' point of view, is the parameter to assess the usefulness of a treatment. In this review, we are thus addressing aspects related to the production and formulation process, highlighting the methods that can be used to evaluate the functioning and impact of microbial-based products on soil microbiome, as tools supporting their use and marketing.

Effects of source sample amount on biodiversity surveys of bacteria, fungi, and nematodes in soil ecosystems

Bacteria, fungi, and nematodes are major components of soil ecosystems, playing pivotal roles in belowground material cycles and biological community processes. A number of studies have recently uncovered the diversity and community structure of those organisms in various types of soil ecosystems based on DNA metabarcoding (amplicon sequencing). However, because most previous studies examined only one or two of the three organismal groups, it remains an important challenge to reveal the entire picture of soil community structure. We examined how we could standardize DNA extraction protocols for simultaneous DNA metabarcoding of bacteria, fungi, and nematodes. Specifically, in an Illumina sequencing analysis of forest and farmland soil samples, we performed DNA extraction at five levels of soil-amount (0.5, 2, 5, 10, and 20 g). We then found that DNA extraction with the 0.5 g soil setting, which had been applied as default in many commercial DNA extraction kits, could lead to underestimation of α-diversity in nematode community. We also found that dissimilarity (β-diversity) estimates of community structure among replicate samples could be affected by soil sample amount. Based on the assays, we conclude that DNA extraction from at least 20 g of soil is a standard for comparing biodiversity patterns among bacteria, fungi and nematodes.

The effect of soil sample size, for practical DNA extraction, on soil microbial diversity in different taxonomic ranks

To determine the optimal soil sample size for microbial community structure analysis, DNA extraction, microbial composition analysis, and diversity assessments were performed using soil sample sizes of 0.2, 1, and 5 g. This study focused on the relationship between soil amount and DNA extraction container volume and the alteration in microbial composition at different taxonomic ranks (order, class, and phylum). Horizontal (0.2 and 1 g) and vertical (5 g) shaking were applied during DNA extraction for practical use in a small laboratory. In the case of the 5 g soil sample, DNA extraction efficiency and the value of α-diversity index fluctuated severely, possibly because of vertical shaking. Regarding the 0.2 and 1 g soil samples, the number of taxa, Shannon–Wiener index, and Bray–Curtis dissimilarity were stable and had approximately the same values at each taxonomic rank. However, non-metric multidimensional scaling showed that the microbial compositions of these two sample sizes were different. The higher relative abundance of taxa in the case of the 0.2 g soil sample might indicate that cell wall compositions differentiated the microbial community structures in these two sample sizes due to high shear stress tolerance. The soil sample size and tube volume affected the estimated microbial community structure. A soil sample size of 0.2 g would be preferable to the other sample sizes because of the possible higher shearing force for DNA extraction and lower experimental costs due to smaller amounts of consumables. When the taxonomic rank was changed from order to phylum, some minor taxa identified at the order rank were integrated into major taxa at the phylum rank. The integration affected the value of the β-diversity index; therefore, the microbial community structure analysis, reproducibility of structures, diversity assessment, and detection of minor taxa would be influenced by the taxonomic rank applied.

Unearthing the Potential of Soil eDNA Metabarcoding—Towards Best Practice Advice for Invertebrate Biodiversity Assessment

Metabarcoding has proven to be a powerful tool to assess ecological patterns and diversity from different habitats. Terrestrial invertebrate diversity is frequently based on bulk samples, which require comparatively high sampling effort. With environmental DNA (eDNA) metabarcoding, field sampling effort can be reduced while increasing the number of recovered organism groups. However, a proof of concept is missing for several invertebrate groups, hampering the development of best-practice advice for these groups. This study aims to provide recommendations on key aspects for the processing of soil samples, from sampling effort to choice of DNA extraction method and marker genes. This study uses eDNA metabarcoding as a tool for assessing invertebrate biodiversity in soil samples, specifically comparing two DNA extraction methods (with and without a lysis step) and two genes, 18S and COI markers. The results show that the choice of marker and DNA extraction method (including a lysis step) significantly affect species detection rates and concomitantly observed invertebrate community composition. Combining methods, by using larger amounts of starting material and including a lysis step resulted in an increase of invertebrate species numbers. Together, these methods improved the detection of species with known lower population densities and allowed the assessment of temporary mesofauna. Furthermore, the choice of marker significantly influenced the diversity levels found. The 18S marker allowed the detection of a higher number of annelid and nematode OTUs, while the COI marker was more suitable for detecting changes in arthropod community structure, especially at the species level. This study makes significant advances to the field of invertebrate biodiversity assessment, particularly using metabarcoding tools by addressing several methodological considerations that are key for accurate ecological appraisals.

Sampling forest soils to describe fungal diversity and composition. Which is the optimal sampling size in mediterranean pure and mixed pine oak forests?

Soil sampling is a critical step affecting perceived fungal diversity, however sampling optimization for high-throughput-DNA sequencing studies have never been tested in Mediterranean forest ecosystems. We identified the minimum number of pooled samples needed to obtain a reliable description of fungal communities in terms of diversity and composition in three different Mediterranean forests (pine, oak, and mixed-pine-oak). Twenty soil samples were randomly selected in each of the three plots per type. Samples obtained in 100 m² plots were pooled to obtain mixtures of 3, 6, 10, 15, 20 samples, and sequenced using Illumina MiSeq of fungal ITS2 amplicons. Pooling three soil samples in Pinus and Quercus stands provided consistent richness estimations, while at least six samples were needed in mixed-stands. β-diversity decreased with increasing sample pools in monospecific-stands, while there was no effect of sample pool size on mixed-stands. Soil sample pooling had no effect over species composition. We estimate that three samples would be already optimal to describe fungal richness and composition in Mediterranean pure stands, while at least six samples would be needed in mixed stands.

Impacts of Sampling Design on Estimates of Microbial Community Diversity and Composition in Agricultural Soils

Soil microbiota play important and diverse roles in agricultural crop nutrition and productivity. Yet, despite increasing efforts to characterize soil bacterial and fungal assemblages, it is challenging to disentangle the influences of sampling design on assessments of communities. Here, we sought to determine whether composite samples-often analyzed as a low cost and effort alternative to replicated individual samples-provide representative summary estimates of microbial communities. At three Minnesota agricultural research sites planted with an oat cover crop, we conducted amplicon sequencing for soil bacterial and fungal communities (16S_V4 and ITS2) of replicated individual or homogenized composite soil samples. We compared soil microbiota from within and among plots and then among agricultural sites using both sampling strategies. Results indicated that single or multiple replicated individual samples, or a composite sample from each plot, were sufficient for distinguishing broad site-level macroecological differences among bacterial and fungal communities. Analysis of a single sample per plot captured only a small fraction of the distinct OTUs, diversity, and compositional variability detected in the analysis of multiple individual samples or a single composite sample. Likewise, composite samples captured only a fraction of the diversity represented by the six individual samples from which they were formed, and, on average, analysis of two or three individual samples offered greater compositional coverage (i.e., greater number of OTUs) than a single composite sample. We conclude that sampling design significantly impacts estimates of bacterial and fungal communities even in homogeneously managed agricultural soils, and our findings indicate that while either strategy may be sufficient for broad macroecological investigations, composites may be a poor substitute for replicated samples at finer spatial scales.

Sample size effects on the assessment of eukaryotic diversity and community structure in aquatic sediments using high-throughput sequencing

Understanding how biodiversity changes in time and space is vital to assess the effects of environmental change on benthic ecosystems. Due to the limitations of morphological methods, there has been a rapid expansion in the application of high-throughput sequencing methods to study benthic eukaryotic communities. However, the effect of sample size and small-scale spatial variation on the assessment of benthic eukaryotic diversity is still not well understood. Here, we investigate the effect of different sample volumes in the genetic assessment of benthic metazoan and non-metazoan eukaryotic community composition. Accordingly, DNA was extracted from five different cumulative sediment volumes comprising 100% of the top 2 cm of five benthic sampling cores, and used as template for Ilumina MiSeq sequencing of 18 S rRNA amplicons. Sample volumes strongly impacted diversity metrics for both metazoans and non-metazoan eukaryotes. Beta-diversity of treatments using smaller sample volumes was significantly different from the beta-diversity of the 100% sampled area. Overall our findings indicate that sample volumes of 0.2 g (1% of the sampled area) are insufficient to account for spatial heterogeneity at small spatial scales, and that relatively large percentages of sediment core samples are needed for obtaining robust diversity measurement of both metazoan and non-metazoan eukaryotes.

High-throughput Sequencing of Trace Quantities of Soil Provides Reproducible and Discriminative Fungal DNA Profiles

High-throughput sequencing (HTS) offers improved resolution between forensic soil samples by characterizing individual taxa present; however, the heterogeneous distribution of taxa in soils, and limited quantity of material available, may hinder the reliability of HTS in casework. Using HTS of the internal transcribed spacer, we examined the effect of soil mass (50, 150, and 250 mg) on fungal DNA profiles, focusing on reproducibility and discriminatory power between close proximity soils, and samples with similar textural classification. The results show that reduced soil mass had no significant effect on sample differentiation and that 150 mg soil provides the most reproducible DNA profiles across different soil types. In addition, Ascomycota was identified as a robust fungal target for forensic intelligence as this phylum was detected consistently across all samples regardless of sample quantity. Overall, this study highlights the value of trace quantities of soil for use in forensic casework.

Size Matters: Assessing Optimum Soil Sample Size for Fungal and Bacterial Community Structure Analyses Using High Throughput Sequencing of rRNA Gene Amplicons

We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5, and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI) were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungal community structure, replicate dispersion and the number of operational taxonomic units (OTUs) retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation.

A review: what is the spermosphere and how can it be studied?

The spermosphere is the zone surrounding seeds where interactions between the soil, microbial communities and germinating seeds take place. The concept of the spermosphere is usually only applied during germination sensu stricto. Despite the transient nature of this very small zone of soil around the germinating seed, the microbial activities which occur there may have long-lasting impacts on plants. The spermosphere is indirectly characterized by either (i) seed exudates, which could be inhibitors or stimulators of micro-organism growth or (ii) the composition of the microbiome on and around the germinating seeds. The microbial communities present in the spermosphere directly reflect that of the germination medium or are host-dependent and influenced quantitatively and qualitatively by host exudates. Despite its strong impact on the future development of plants, the spermosphere remains little studied. This can be explained by the technical difficulties related to characterizing this concept due to its short duration, small size and biomass, and the number and complexity of the interactions that take place. However, recent technical methods, such as metabolite profiling, combining phenotypic methods with DNA- and RNA-based methods, could be used to investigate seed exudates, microbial communities and their interactions with the soil environment.

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Subgingival microbiome in smokers and non-smokers in Korean chronic periodontitis patients

Smoking is a major environmental factor associated with periodontal diseases. However, we still have a very limited understanding of the relationship between smoking and subgingival microflora in the global population. Here, we investigated the composition of subgingival bacterial communities from the pooled plaque samples of smokers and non-smokers, 134 samples in each group, in Korean patients with moderate chronic periodontitis using 16S rRNA gene-based pyrosequencing. A total of 17,927 reads were analyzed and classified into 12 phyla, 126 genera, and 394 species. Differences in bacterial communities between smokers and non-smokers were examined at all phylogenetic levels. The genera Fusobacterium, Fretibacterium, Streptococcus, Veillonella, Corynebacterium, TM7, and Filifactor were abundant in smokers. On the other hand, Prevotella, Campylobacter, Aggregatibacter, Veillonellaceae GQ422718, Haemophilus, and Prevotellaceae were less abundant in smokers. Among species-level taxa occupying > 1% of whole subgingival microbiome of smokers, higher abundance (≥ 2.0-fold compared to non-smokers) of seven species or operational taxonomic units (OTUs) was found: Fusobacterium nucleatum, Neisseria sicca, Neisseria oralis, Corynebacterium matruchotii, Veillonella dispar, Filifactor alocis, and Fretibacterium AY349371. On the other hand, lower abundance of 11 species or OTUs was found in smokers: Neisseria elongata, six Prevotella species or OTUs, Fusobacterium canifelinum, Aggregatibacter AM420165, Selenomonas OTU, and Veillonellaceae GU470897. Species richness and evenness were similar between the groups whereas diversity was greater in smokers than non-smokers. Collectively, the results of the present study indicate that differences exist in the subgingival bacterial community between smoker and non-smoker patients with chronic moderate periodontitis in Korea, suggesting that cigarette smoking considerably affects subgingival bacterial ecology.

Use of pressure cycling technology for cell lysis and recovery of bacterial and fungal communities from soil

Selection of cell lysis methodology is critical to microbial community analyses due to the inability of any single extraction technology to recover the absolute genetic structure from environmental samples. Numerous methodologies are currently applied to interrogate soil communities, each with its own inherent bias. Here we compared the efficacy and bias of three physical cell lysis methods in conjunction with the PowerLyzer PowerSoil DNA Isolation Kit (MO BIO) for direct DNA extraction from soil: bead-beating, vortex disruption, and hydrostatic pressure cycling technology (PCT). PCT lysis, which is relatively new to soil DNA extraction, was optimized for soils of two different textures prior to comparison with traditional bead-beating and vortex disruption lysis. All cell lysis methods successfully recovered DNA. Although the two traditional mechanical lysis methods yielded greater genomic, bacterial, and fungal DNA per gram soil than the PCT method, the latter resulted in a greater number of unique terminal restriction fragments by terminal RFLP (T-RFLP) analysis. These findings indicate the importance of diversity and quantity measures when assessing DNA extraction bias, as soil DNA retrieved by PCT lysis represented populations not found using traditional mechanical lysis methods.

Uneven distribution of Halobacillus trueperi species in arid natural saline systems of Southern Tunisian Sahara

The genetic diversity of a collection of 336 spore-forming isolates recovered from five salt-saturated brines and soils (Chott and Sebkhas) mainly located in the hyper-arid regions of the southern Tunisian Sahara has been assessed. Requirements and abilities for growth at a wide range of salinities\ showed that 44.3 % of the isolates were extremely halotolerant, 23 % were moderate halotolerant, and 32.7 % were strict halophiles, indicating that they are adapted to thrive in these saline ecosystems. A wide genetic diversity was documented based on 16S-23S rRNA internal transcribed spacer fingerprinting profiles (ITS) and 16S rRNA gene sequences that clustered the strains into seven genera: Bacillus, Gracilibacillus, Halobacillus, Oceanobacillus, Paenibacillus, Pontibacillus, and Virgibacillus. Halobacillus trueperi was the most encountered species in all the sites and presented a large intraspecific diversity with a multiplicity of ITS types. The most frequent ITS type included 42 isolates that were chosen for assessing of the intraspecific diversity by BOX-PCR fingerprinting. A high intraspecific microdiversity was documented by 14 BOX-PCR genotypes whose distribution correlated with the strain geographic origin. Interestingly, H. trueperi isolates presented an uneven geographic distribution among sites with the highest frequency of isolation from the coastal sites, suggesting a marine rather than terrestrial origin of the strains. The high frequency and diversity of H. trueperi suggest that it is a major ecosystem-adapted microbial component of the Tunisian Sahara harsh saline systems of marine origin.

Microbes on building materials--evaluation of DNA extraction protocols as common basis for molecular analysis

The study of microbial life in building materials is an emerging topic concerning biodeterioration of materials as well as health risks in houses and at working places. Biodegradation and potential health implications associated with microbial growth in our residues claim for more precise methods for quantification and identification. To date, cultivation experiments are commonly used to gain insight into the microbial diversity. Nowadays, molecular techniques for the identification of microorganisms provide efficient methods that can be applied in this field. The efficiency of DNA extraction is decisive in order to perform a reliable and reproducible quantification of the microorganisms by qPCR or to characterize the structure of the microbial community. In this study we tested thirteen DNA extraction methods and evaluated their efficiency for identifying (1) the quantity of DNA, (2) the quality and purity of DNA and (3) the ability of the DNA to be amplified in a PCR reaction using three universal primer sets for the ITS region of fungi as well as one primer pair targeting the 16S rRNA of bacteria with three typical building materials - common plaster, red brick and gypsum cardboard. DNA concentration measurements showed strong variations among the tested methods and materials. Measurement of the DNA yield showed up to three orders of magnitude variation from the same samples, whereas A260/A280 ratios often prognosticated biases in the PCR amplifications. Visualization of the crude DNA extracts and the comparison of DGGE fingerprints showed additional drawbacks of some methods. The FastDNA Spin kit for soil showed to be the best DNA extraction method and could provide positive results for all tests with the three building materials. Therefore, we suggest this method as a gold standard for quantification of indoor fungi and bacteria in building materials.

Quantification of extraradical soil mycelium and ectomycorrhizas of Boletus edulis in a Scots pine forest with variable sporocarp productivity

The availability of most edible ectomycorrhizal mushrooms depends on their natural fructification. Sporocarp formation of these fungi is linked to habitat characteristics and climate conditions, but these data alone do not explain all the trends of fungal fruiting and dynamics. It could be hypothesized that the amount of soil mycelia could also be related to the production of carpophores. Soil samples (five cylinders of 250 cm(3) per plot) were taken monthly, from September to November, in five fenced permanent plots (5 × 5 m) in Pinar Grande (Soria, Spain), a Pinus sylvestris stand situated in the north of the Sistema Ibérico mountain range. Plots were chosen to establish a gradient of Boletus edulis productivity from 0 to 38.5 kg/ha year, according to the mean fresh weight of sporocarps collected during the last 10 years. B. edulis ectomycorrhizal root tips were identified in each soil sample according to its morphology and counted. DNA extractions were performed with the PowerSoil(TM) DNA Isolation Kit and quantification of extraradical soil mycelium by real-time polymerase chain reaction using specific primers and a TaqMan® probe. The concentration of soil mycelium of B. edulis (mg mycelium/g soil) did not differ significantly between plots (p = 0.1397), and sampling time (p = 0.7643) within the fructification period. The number of mycorrhizal short roots per soil volume showed significant differences between the plots (p = 0.0050) and the three sampling times (p < 0.0001). No significant correlation between the number of mycorrhizas and the productivity of the plot (kg of B. edulis/ha year) was detected (p = 0.615). A statistically significant positive correlation (p = 0.0481) was detected between the concentration of mycelia of B. edulis in the soil samples and the presence of short roots mycorrhizal with B. edulis in these samples. The productivity of the plots, in terms of sporocarps produced during the last 10 years, was not correlated either with the concentration of soil mycelium or with the presence or abundance of ectomycorrhizas.

Soil-specific limitations for access and analysis of soil microbial communities by metagenomics

Metagenomics approaches represent an important way to acquire information on the microbial communities present in complex environments like soil. However, to what extent do these approaches provide us with a true picture of soil microbial diversity? Soil is a challenging environment to work with. Its physicochemical properties affect microbial distributions inside the soil matrix, metagenome extraction and its subsequent analyses. To better understand the bias inherent to soil metagenome 'processing', we focus on soil physicochemical properties and their effects on the perceived bacterial distribution. In the light of this information, each step of soil metagenome processing is then discussed, with an emphasis on strategies for optimal soil sampling. Then, the interaction of cells and DNA with the soil matrix and the consequences for microbial DNA extraction are examined. Soil DNA extraction methods are compared and the veracity of the microbial profiles obtained is discussed. Finally, soil metagenomic sequence analysis and exploitation methods are reviewed.

Negative effects of sample pooling on PCR-based estimates of soil microbial richness and community structure

In this study, we examined the effect of various pooling strategies on the characterization of soil microbial community composition and phylotype richness estimates. Automated ribosomal intergenic spacer analysis (ARISA) profiles were determined from soil samples that were (i) unpooled (extracted and amplified individually), (ii) pooled prior to PCR amplification, or (iii) pooled prior to DNA extraction. Regression analyses suggest that the less even the soil microbial community (i.e., low Shannon equitability, E(H)), the greater was the impact of either pooling strategy on microbial detection (R(2) = 0.766). For example, at a tropical rainforest site, which had the most uneven fungal (E(H) of 0.597) and bacterial communities (E(H) of 0.822), the unpooled procedure detected an additional 67 fungal and 115 bacterial phylotypes relative to either of the pooled procedures. Phylotype rarity, resulting in missed detection upon pooling, differed between the fungal and bacterial communities. Fungi were typified by locally abundant but spatially rare phylotypes, and the bacteria were typified by locally rare but spatially ubiquitous phylotypes. As a result, pooling differentially influenced plot comparisons, leading to an increase in similarity for the bacterial community and a decrease in the fungal community. In conclusion, although pooling reduces sample numbers and variability, it could mask a significant portion of the detectable microbial community, particularly for fungi due to their higher spatial heterogeneity.